Learning Fault-Tolerant Locomotion with Adaptive Gait Timing
Giovanbattista Gravina, Luca Rossini, Carlo Rizzardo, Arturo Laurenzi, Nikos Tsagarakis
cs.RO, cs.LG
Submitted: 2026-08-07
Updated: 2026-08-10
Comments: Accepted at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Project page: https://gianni0907.github.io/fault_tolerant_locomotion
License: http://creativecommons.org/licenses/by/4.0/
The gist: Hardware failures require legged robots to rapidly reorganize coordination and gait timing to maintain stability and mobility.
Terminology
Abstract
Hardware failures require legged robots to rapidly reorganize coordination and gait timing to maintain stability and mobility. This is particularly challenging for larger quadrupeds, where increased mass and tighter actuation limits reduce the feasibility of aggressive, high-frequency compensation strategies often observed on smaller platforms. In this work, we propose a deep reinforcement learning approach for fault-tolerant locomotion under actuator power loss. The method employs an asymmetric actor-critic architecture in which the critic has access to privileged information during training, while the actor learns to reconstruct a corresponding latent representation from proprioceptive observations. We introduce a latent-alignment loss that encourages consistency between actor and critic representations. Additionally, we augment the action space with a learnable gait frequency parameter, enabling adaptive gait timing in response to terrain variations and actuator degradation without predefined faulty-leg strategies. The approach is validated in high-fidelity simulation on uneven terrain and real-world experiments on flat ground using a 68 kg quadruped robot.
Sources
- KYON: Semi-Modular Wheel-Legged Quadruped With Agile Bimanual Capability
- Asymmetric Actor Critic for Image-Based Robot Learning
- AcL: Action Learner for Fault-Tolerant Quadruped Locomotion Control
- Proximal Policy Optimization Algorithms
- Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
- Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving